Literature DB >> 30417863

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations.

Mikael N E Sommarin1, Rebecca Warfvinge2, Fatemeh Safi2, Göran Karlsson3.   

Abstract

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is inherently a single-cell assay, however prior to molecular analysis, the target cells are often prospectively isolated in bulk, thereby losing single-cell resolution. Single-cell gene expression analysis provides a means to understand molecular differences between individual cells in heterogeneous cell populations. In bulk cell analysis an overrepresentation of a distinct cell type results in biases and occlusions of signals from rare cells with biological importance. By utilizing FACS index sorting coupled to single-cell gene expression analysis, populations can be investigated without the loss of single-cell resolution while cells with intermediate cell surface marker expression are also captured, enabling evaluation of the relevance of continuous surface marker expression. Here, we describe an approach that combines single-cell reverse transcription quantitative PCR (RT-qPCR) and FACS index sorting to simultaneously characterize the molecular and immunophenotypic heterogeneity within cell populations. In contrast to single-cell RNA sequencing methods, the use of qPCR with specific target amplification allows for robust measurements of low-abundance transcripts with fewer dropouts, while it is not confounded by issues related to cell-to-cell variations in read depth. Moreover, by directly index-sorting single-cells into lysis buffer this method, allows for cDNA synthesis and specific target pre-amplification to be performed in one step as well as for correlation of subsequently derived molecular signatures with cell surface marker expression. The described approach has been developed to investigate hematopoietic single-cells, but have also been used successfully on other cell types. In conclusion, the approach described herein allows for sensitive measurement of mRNA expression for a panel of pre-selected genes with the possibility to develop protocols for subsequent prospective isolation of molecularly distinct subpopulations.

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Year:  2018        PMID: 30417863      PMCID: PMC6235599          DOI: 10.3791/57831

Source DB:  PubMed          Journal:  J Vis Exp        ISSN: 1940-087X            Impact factor:   1.355


  27 in total

Review 1.  Hematopoietic stem cell: self-renewal versus differentiation.

Authors:  Jun Seita; Irving L Weissman
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2010 Nov-Dec

2.  Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels.

Authors:  Martin Bengtsson; Anders Ståhlberg; Patrik Rorsman; Mikael Kubista
Journal:  Genome Res       Date:  2005-10       Impact factor: 9.043

3.  Single-cell gene profiling of planarian stem cells using fluorescent activated cell sorting and its "index sorting" function for stem cell research.

Authors:  Tetsutaro Hayashi; Norito Shibata; Ryo Okumura; Tomomi Kudome; Osamu Nishimura; Hiroshi Tarui; Kiyokazu Agata
Journal:  Dev Growth Differ       Date:  2010-01       Impact factor: 2.053

Review 4.  Spatially resolved transcriptomics and beyond.

Authors:  Nicola Crosetto; Magda Bienko; Alexander van Oudenaarden
Journal:  Nat Rev Genet       Date:  2014-12-02       Impact factor: 53.242

5.  Single-cell RNA sequencing reveals developmental heterogeneity among early lymphoid progenitors.

Authors:  Llucia Alberti-Servera; Lilly von Muenchow; Panagiotis Tsapogas; Giuseppina Capoferri; Katja Eschbach; Christian Beisel; Rhodri Ceredig; Robert Ivanek; Antonius Rolink
Journal:  EMBO J       Date:  2017-10-13       Impact factor: 11.598

6.  A cellular oncogene is translocated to the Philadelphia chromosome in chronic myelocytic leukaemia.

Authors:  A de Klein; A G van Kessel; G Grosveld; C R Bartram; A Hagemeijer; D Bootsma; N K Spurr; N Heisterkamp; J Groffen; J R Stephenson
Journal:  Nature       Date:  1982-12-23       Impact factor: 49.962

7.  SCExV: a webtool for the analysis and visualisation of single cell qRT-PCR data.

Authors:  Stefan Lang; Amol Ugale; Eva Erlandsson; Göran Karlsson; David Bryder; Shamit Soneji
Journal:  BMC Bioinformatics       Date:  2015-10-05       Impact factor: 3.169

Review 8.  Studying hematopoiesis using single-cell technologies.

Authors:  Fang Ye; Wentao Huang; Guoji Guo
Journal:  J Hematol Oncol       Date:  2017-01-21       Impact factor: 17.388

9.  Batch effects and the effective design of single-cell gene expression studies.

Authors:  Po-Yuan Tung; John D Blischak; Chiaowen Joyce Hsiao; David A Knowles; Jonathan E Burnett; Jonathan K Pritchard; Yoav Gilad
Journal:  Sci Rep       Date:  2017-01-03       Impact factor: 4.379

10.  Quantification of mRNA in single cells and modelling of RT-qPCR induced noise.

Authors:  Martin Bengtsson; Martin Hemberg; Patrik Rorsman; Anders Ståhlberg
Journal:  BMC Mol Biol       Date:  2008-07-17       Impact factor: 2.946

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